Systems and methods for risk assessment and treatment planning of arterio-venous malformation

ABSTRACT

A computer implemented method for assessing an arterio-venous malformation (AVM) may include, for example, receiving a patient-specific model of a portion of an anatomy of a patient; using a computer processor to analyze the patient-specific model for identifying one or more blood vessels associated with the AVM, in the patient-specific model; and estimating a risk of an undesirable outcome caused by the AVM, by performing computer simulations of blood flow through the one or more blood vessels associated with the AVM in the patient-specific model.

RELATED APPLICATIONS

This application claims priority under 35 U.S.C. § 119(e) to U.S.Provisional Application No. 62/150,701, filed Apr. 21, 2015, entitled“Systems and Methods for Risk Assessment and Treatment Planning ofArterio-Venous Malformation,” the contents of which are incorporatedherein by reference in its entirety.

FIELD OF THE DISCLOSURE

Various embodiments of the present disclosure relate generally to riskassessment and/or treatment planning of arterio-venous malformations(AVM). More specifically, particular embodiments of the presentdisclosure relate to systems and methods for using patient-specificphysiologic information to identify blood vessels relating to an AVM,and then to evaluate a risk of one or more undesirable patient outcomescaused by the AVM and/or treatment plans associated with the AVM.

BACKGROUND

Arterio-venous malformations are abnormal connections between arteriesand veins that may bypass the arterial microcirculation capillaries(capillary arteries). FIG. 1A is a simplified schematic showing a normalarterial and venous network in which an artery 113 receives oxygen-richblood 109 from a heart 108 and delivers the blood 109 to a healthycapillary bed 112, which in turn delivers oxygen-rich blood 109 to thesurrounding tissues. De-oxygenated blood 114 is received by a vein 115from the bed 112, for return to heart 108.

FIG. 1B, on the other hand, is a simplified schematic of an AVM 119. Anartery 113 receives oxygen-rich blood 109 from a heart 108. Blood 109may pass from the artery 113 to the vein 115 via one or morepre-capillary connections 118, the blood 109 thereby bypassing thenormal capillary network 112. In addition, AVM 119 may include a numberof enlarged, engorged vessels 117, including new vessels resulting fromangiogenesis. AVM 119 also may result in an increase in the size of oneor both of artery 113 and vein 115 proximally of AVM 119, and a decreasein the size of artery 113 and vein 115 distally of AVM 119. Further, AVM119 may include one or more collateral connections 116 between portionsof artery 113.

AVMs also may result in less resistance to blood flow, since thepre-capillary arterioles, which provide resistance to blood flow, arebypassed. Patients with AVMs therefore may have a higher blood flow rateand vessels which are larger than normal (such as shown in FIG. 1B,relative to FIG. 1A). The increase in blood flow may result in a highercardiac output demand, which may result in patients with AVMs having ahigher risk of heart failure. The increase in size and number ofvascular channels and the increase in total blood flow to an organ, dueto an AVM, also can result in an increase in tissue perfusion and anincrease in size and growth of tissue. For example, a child with lowerlimb AVMs may have increased size and growth of the affected limb, withincreased bone growth and limb length discrepancy, resulting in theaffected limb being longer and the child walking with a limp. Also, thinwalled veins resulting from AVMs are exposed to high arterial pressure,thus making them vulnerable to rupture and bleeding.

AVMs may be congenital or acquired, and progressive, stable, orregressive. AVMs may be cosmetically undesirable, and may result in anincreased rate of morbidity and/or mortality. Symptoms associated withAVMs may include hemorrhaging, seizures, mass effect (the mass ofenlarged blood vessels causing increased intracranial pressure), pain,swelling, hypertrophy, loss of function, ischemia, embolization, and/orheart failure. AVMs may also be symptomatic or asymptomatic throughout apatient's life. An AVM may occur anywhere in the body, but morefrequently occurs in the brain and the legs.

Radiographic imaging of AVMs may pose challenges due to the multiplicityof overlapping and layered blood vessels, which may result in difficultydiscerning individual blood vessels from each other, and an inability todetermine the direction or flow characteristics in the individual bloodvessels and blood flow within the AVM. Using some imaging techniques,such as radiographic imaging, angiography, or CT scan, AVMs may appearas a white cloudy mass where the lumens of the individual arteriesand/or veins may not be sufficiently delineated.

Thus, a desire exists to obtain improved patient information relating toan AVM, and to provide techniques to assess risk of an AVM and/or planand assess treatment of an AVM.

SUMMARY

According to an embodiment of this disclosure, one computer implementedmethod for assessing an arterio-venous malformation (AVM) includesreceiving a patient-specific model of a portion of an anatomy of apatient; using a computer processor to analyze the patient-specificmodel for identifying one or more blood vessels associated with the AVM,in the patient-specific model; and estimating a risk of an undesirableoutcome caused by the AVM, by performing computer simulations of bloodflow through the one or more blood vessels associated with the AVM inthe patient-specific model.

In embodiments, that method may include one or more of the following:the patient-specific model is based on images of at least a portion of avascular system of the patient; the undesirable outcome includes one ormore of rupture of a blood vessel, a mass effect, an increase in venouspressure, and a change in tissue perfusion; performing blood-flowsimulations includes calculating stresses within or on blood vesselwalls; evaluating one or more treatments of the AVM; identifying one ormore blood vessels for treatment, determining an effect on blood flowcaused by treatment of the one or more blood vessels, and assessing achange the risk of the undesirable outcome; the treatment includes oneor more of embolization of the one or more blood vessels, ablation ofthe one or more blood vessels, surgical removal of all or part of theAVM, or radiosurgery; and/or predicting progression or regression of theAVM.

In accordance with another embodiment, one system for assessing anarterio-venous malformation (AVM) includes a data storage device storinginstructions for assessing an arterio-venous malformation (AVM); and aprocessor configured to execute the instructions to perform a methodincluding the steps: receiving a patient-specific model of a portion ofan anatomy of a patient; using a computer processor to analyze thepatient-specific model for identifying one or more blood vesselsassociated with the AVM, in the patient-specific model; and estimating arisk of an undesirable outcome caused by the AVM, by performing computersimulations of blood flow through the one or more blood vesselsassociated with the AVM in the patient-specific model.

In embodiments, that system may include one or more of the following:the patient-specific model is based on images of at least a portion of avascular system of the patient; the undesirable outcome includes one ormore of rupture of a blood vessel, a mass effect, an increase in venouspressure, and a change in tissue perfusion; performing blood-flowsimulations includes calculating stresses within, on, or external to theblood vessel walls; the processor is further configured for evaluatingone or more treatments of the AVM; evaluating the one or more treatmentsincludes identifying one or more blood vessels for treatment, anddetermining an effect on blood flow caused by treatment of the one ormore blood vessels; the treatment includes one or more of embolizationof the one or more blood vessels, ablation of the one or more bloodvessels, surgical removal of all or part of the AVM, or radiosurgery;and/or the processor is further configured for predicting progression orregression of the AVM.

Another embodiment includes a non-transitory computer-readable mediumstoring instructions that, when executed by a computer, cause thecomputer to perform a method for assessing an arterio-venousmalformation (AVM). The method may include receiving a patient-specificmodel of a portion of an anatomy of a patient; using a computerprocessor to analyze the patient-specific model for identifying one ormore blood vessels associated with the AVM, in the patient-specificmodel; and estimating a risk of an undesirable outcome caused by theAVM, by performing computer simulations of blood flow through the one ormore blood vessels associated with the AVM in the patient-specificmodel.

In embodiments, that method may include one or more of the following:the undesirable outcome includes one or more of rupture of a bloodvessel, a mass effect, an increase in venous pressure, and a change intissue perfusion; evaluating one or more treatments of the AVM,including identifying one or more blood vessels for treatment,determining an effect on blood flow caused by treatment of the one ormore blood vessels and assessing a change the risk of the undesirableoutcome; and predicting progression or regression of the AVM.

Additional objects and advantages of the disclosed embodiments will beset forth in part in the description that follows, and in part will beapparent from the description, or may be learned by practice of thedisclosed embodiments. The objects and advantages on the disclosedembodiments will be realized and attained by means of the elements andcombinations particularly pointed out in the appended claims.

It is to be understood that both the foregoing general description andthe following detailed description are exemplary and explanatory onlyand are not restrictive of the detailed embodiments, as claimed.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this specification, illustrate various exemplary embodiments,and together with the description, serve to explain the principles ofthe disclosed embodiments.

FIGS. 1A and 1B show a normal arterial and venous network, and an AVM,respectively.

FIG. 2 is a block diagram of an exemplary system and network forassessment and/or guiding diagnosis and/or treatment of an AVM,according to an exemplary embodiment of the present disclosure.

FIG. 3 is a flow chart of an exemplary method of assessment and/ortreatment planning of an AVM, according to an exemplary embodiment ofthe present disclosure.

FIG. 4 is a flow chart of an exemplary method of assessment and/ortreatment planning of an AVM, according to an exemplary embodiment ofthe present disclosure.

DESCRIPTION OF THE EMBODIMENTS

Reference will now be made in detail to the exemplary embodiments of thedisclosure, examples of which are illustrated in the accompanyingdrawings. Wherever possible, the same reference numbers will be usedthroughout the drawings to refer to the same or like parts.

In embodiments of this disclosure, a patient specific vascular and/oranatomical model of an AVM may be constructed from one imaging modalityor a combination of imaging modalities. The image acquisition modalitymay be patient and/or AVM specific. While computer tomography (CT) scansor magnetic resonance imaging (MRI) scans may provide sufficient imageresolution for the reconstruction of the arteries and veins in somepatients, these scan types may be supplemented with other procedures(e.g., a more invasive procedure), including selectively performingarteriography, venography, and/or injecting a contrast agent tovisualize the artery and/or vein and enable accurate modeling of the AVMpathways.

Once the patient-specific model has been constructed, blood flowcharacteristics (e.g., blood flow rate, blood pressure, and any relatedquantities, including wall shear stresses) may be calculated by, forexample, assigning appropriate boundary conditions and solving theNavier-Stokes equations. A model of a blood vessel wall with appropriatematerial properties also may be used with fluid-structure interactionsimulations to determine an accurate prediction of intramural stress. Inaddition, modeling growth and remodeling mechanisms in the AVMs may beused to estimate the changes in the blood vessel radius and blood vesselthickness due to intramural and wall shear stresses acting on the bloodvessel walls. Alternatively, reduced order models or machine learningmethods may be used to compute blood flow characteristics. Quantitiesincluding the particle residence time and the oscillatory shear indexmay also be calculated from the solution of the Navier-Stokes equations.

Estimates of patient risk due to the AVM may be based on (a) purelyanatomical information, and/or metabolic and physiologic information,and/or (b) functional analysis through blood flow simulations. A riskestimate based on anatomical information may depend on the location ofthe AVM, its proximity to critical organs and/or blood vessels (greaterrisk when the AVM is proximate to an organ or vessel), the difficulty inaccessing the AVM locations within the body (difficult to reach AVMs maycause an increased risk of an undesirable event during the treatmentprocedure), the size (diameter) of the feeding arteries, branches,collaterals, connecting vessels and draining veins; the location ofvessels; the thickness and/or the composition of the vessel wall; thesize, location and/or number of communicating channels between theartery and the vein; the organ or other anatomical structure beingsupplied by the involved vessels; and the anatomy of surroundingstructures or organs. The risk of blood vessel rupture may be determinedbased on blood flow simulations and vessel wall characteristics,including thickness, structure, and/or composition of the vessel wall.Thin walled veins and degenerated or injured vessels are more prone torupture. For example, vessel rupture risk may be increased by forcesacting within or on the blood vessel walls, including intramural andtransmural stresses acting on the vessel wall, and/or decreases instrength or structural integrity of the wall. Wall shear stresses canstimulate vessel enlargement, which can be accompanied by thinning ofthe wall, also leading to rupture. Forces acting on blood vessel wallsmay be calculated using the blood pressure and the tangential gradientof blood velocity along the vascular walls, and solid mechanics ofvessel wall characteristics.

The benefits of one or more treatments, including interventionalprocedures such as embolization of the blood vessels feeding the AVM,may be estimated/determined by evaluating one or more of the reductionin AVM blood flow, transmural, intramural, and wall shear stresses, andthe restoration of venous pressure and tissue perfusion to nominalvalues. This may be performed by first modeling the patient-specificanatomical geometry post-treatment, performing hemodynamic simulations(or using reduced order models and/or machine learning methods), andsubsequently, evaluating the factors that were quantified at apre-treatment stage as compared to the post-treatment stage. The riskfactor of no intervention may also be quantified by evaluating the abovecriteria without any modification in the patient-specific anatomicalgeometry.

Any of the methods and systems disclosed in U.S. Pat. Nos. 8,315,812 and9,042,613 and U.S. Patent Application Publication No. 2014/0073977, forpreparing the patient-specific models from, for example patient-specificphysiologic images/data, determining blood flow characteristics,performing blood flow simulations, and/or evaluating pre- andpost-treatment planning may be used with the methods and systems of thepresent disclosure. The complete disclosures of these patents andpublished application are incorporated by reference herein in theirentirety.

FIG. 2 depicts a block diagram of an exemplary system 100 fordiagnosing, assessing, and/or treatment planning of an AVM, according toan exemplary embodiment. Specifically, FIG. 2 depicts a plurality ofphysicians 102 and third-party providers 104, any of whom may beconnected to an electronic network 101, such as the Internet, throughone or more computers, servers, and/or handheld mobile devices.Physicians 102 and/or third-party providers 104 may create or otherwiseobtain images of one or more patients' anatomies. The physicians 102and/or third-party providers 104 also may obtain any combination ofpatient-specific information, such as age, medical history, bloodpressure, blood viscosity, patient activity or exercise level, etc.Physicians 102 and/or third-party providers 104 may transmit theanatomical images and/or patient-specific information to server systems106 over the electronic network 101. Server systems 106 may includestorage devices for storing images and data received from physicians 102and/or third-party providers 104. Server systems 106 may also includeprocessing devices for processing images and data stored in the storagedevices.

FIG. 3 depicts an embodiment of an exemplary method 200 for assessmentand/or treatment planning of an AVM. The method of FIG. 3 may beperformed by server systems 106, based on information, images, and datareceived from physicians 102 and/or third-party providers 104 overelectronic network 101.

In an embodiment, step 202 may include receiving physiologic informationof a patient. The physiologic information may include imaging data of apatient's brain, leg, or other portion of the body. The data may derivefrom one or more imaging modalities (e.g., computed tomography (CT)scans, magnetic resonance imaging (MRI), ultrasound) and othervisualization techniques, including arteriography, venography, and/orinjecting a contrast agent to visualize the artery and/or vein. Otherphysiologic information that may be received includes blood flow ratesand/or blood pressure measurements at one or more locations within thetarget vasculature. The received physiologic information may be storedin an electronic storage medium including, but not limited to, a harddrive, network drive, cloud drive, mobile phone, tablet, or the like.

In one embodiment, step 203 may include generating a patient-specificgeometric model and/or vascular model from the patient-specificphysiologic information based on any known technique, including thetechniques described in U.S. Pat. No. 8,315,812 incorporated above. Thepatient-specific model may be generated at the server system 106 orelsewhere and received over an electronic network (e.g., electronicnetwork 101).

In one embodiment, step 204 may include identifying the location of theone or more AVMs, and performing segmentation of the blood flow lumensleading to and from the AVM. For example, step 204 may includeidentifying and optionally highlighting and displaying the AVM, some orall of the vessels feeding blood to the AVM from one or more arteries,some or all of the vessels draining blood from the AVM to one or moreveins, and any corresponding parent blood vessels. The display mayresemble FIG. 1B, for example.

After identifying the AVM and related vessels in step 204, a methodaccording to an embodiment of this disclosure branches into one or bothof two steps, one step evaluating treatment options and another stepassessing the risk of not treating the AVM. One or both of these stepsmay be performed in embodiments of this disclosure.

The treatment steps are shown in steps 206 and 208 of FIG. 3. Step 206may include identifying and optionally displaying one or more of theblood vessels identified in step 204 that will undergo a treatment. Forexample, the identified blood vessels may include one or more feedingvessels, draining vessels, or corresponding parent vessels.

In one embodiment, step 208 may include determining the flow effect ofthe one or more treatment modalities in the one or more identifiedvessels. The treatment may include embolization or ablation of one ormore vessels, surgical removal of all or part of the AVM, radiosurgery,or any other method that affects the blood flow within the vessels/AVM,including, for example, elimination of the direct communication betweenartery and vein by a direct repair. A direct repair can be performed,for example in a traumatic AV fistula where the hole in the artery andvein are repaired, or when an AV dialysis shunt is removed and thevessels are repaired. For example, in some embodiments, step 208 mayinclude quantifying the benefit and/or risk of embolization of thefeeding vessels. Step 208 may further include providing and assessing avariety of treatment options relative to one another and/or relative tonot treating the AVM and surrounding vessels. For example, the treatmentstrategy may involve either the embolization of a subset of the feedingblood vessels, a subset of the draining blood vessels, a combinationthereof, or excising all or a portion of the AVM. Such combinations maybe explored exhaustively or by first identifying the larger bloodvessels of the AVM and sequentially progressing to the smaller bloodvessels until a desired hemodynamic state is achieved. Treatmentbenefits may be computed by simulating patient-specific vasculargeometry post treatment. Blood flow simulations may be performed in themodified geometry by, for example, solving 3D Navier-Stokes equations,using reduced order models, or using machine learning methods. Any ofthe methods and systems disclosed in U.S. Pat. Nos. 8,315,812 and9,042,613 and U.S. Patent Application Publication No. 2014/0073977incorporated above, for performing blood flow simulations may be usedwith the methods and systems of the present disclosure. In addition, asdescribed below, a number of quantities may be calculated based on nottreating the AVM or surrounding vessels. Those quantities, such asrupture risk, venous pressure, and capillary perfusion levels, for eachtreatment strategy may be calculated in step 208.

As mentioned above, embodiments of this disclosure may assess the riskof undesirable outcomes if no treatment is performed on the AVM.Undesirable outcomes include blood vessel rupture, mass effects, highvenous pressure, and alteration in tissue perfusion. The step ofassessing the risk of these outcomes, shown as step 210 in FIG. 3, maybe performed with or without treatment steps 206, 208. As with thetreatment steps, steps 202, 203, and 204 are performed first.

In some embodiments, step 210 includes quantifying the risk of anundesirable outcome (rupture, mass effect, etc.) with no treatment ofthe AVM, by performing blood-flow simulations, including for example,calculating intramural and wall shear stresses and/or vessel wallproperties (e.g. strength of the vessel wall) from blood flowsimulations. Patient risk may be quantified by either using anatomicalinformation, functional information, or a combination thereof. The riskof a rupture of a vessel may be based on the probability that the AVMmay rupture around a vital organ or artery, or compress or otherwiseimpact the surrounding tissue. The rupture risk may be representative ofinternal and external forces acting on the walls of the one or moreblood vessels which comprise the AVM.

The risk of rupture may be quantified by first performingpatient-specific simulations of blood flow using boundary conditionsderived from either (a) flow rates and/or internal pressures measured instep 203 or (b) resistance boundary conditions scaled based on the sizeof the AVM and the feeding and draining vessels, vessel wallcharacteristics, and extravascular forces acting on the vessels and/orAVM. Alternatively, reduced order models or machine learning methods maybe used to perform blood flow simulations. The blood flow rates and/orblood pressures from the hemodynamic simulations may be used tocalculate the wall shear stress and intramural stress within one or moreof the plurality of blood vessels. The wall shear stress may becalculated by calculating the magnitude of the derivative of bloodvelocity along the blood vessel walls, while intramural stress may becalculated using the blood pressure and the radius and thickness of theblood vessel (vessel thickness may be modeled as 10% of radius if ameasurement is unavailable). Alternatively, intramural stresses may becalculated by performing fluid-structure interaction simulations byassuming a Young's modulus for the blood vessel wall. The risk of bloodvessel rupture may be quantified by the probability that the net wallstress may exceed the wall strength.

An undesirable mass effect that may result by not treating an AVM mayinclude the compression, displacement, encroachment, or irritant (i.e.,trigger source for seizures) of blood vessels. The mass effect may beincluded in an analysis by modeling the stiffness of surrounding tissuesand/or structures to model the interaction of various forces on the AVMin relation to surrounding tissues. Mass effects may be quantified bythe displacement of the AVM and the net forces exerted on thesurrounding tissues.

In embodiments of the disclosure, after assessing treatment options insteps 206, 208 and/or undesirable outcomes of no treatment in step 210,step 212 may include performing growth and remodeling simulations topredict the new homeostatic vessel state. Step 212 may includepredicting progression and/or regression of the AVM. The progressionand/or regression of the AVM may be modeled by solving stressequilibrium equations of the blood vessel walls, and computing the AVMconfiguration (radius and thickness of the blood vessels) at ahomeostatic state. This may be calculated, for example, by firstevaluating the wall shear stresses and the intramural stresses from theblood flow simulations. Subsequently, the remodeling of the lumengeometry may be performed using either reduced order models, finiteelement simulations, machine learning methods, or a combination thereof.Reduced order one dimensional equations for predicting the remodeling ofradius and thickness of blood vessels may be written as

${P = {{\frac{\sigma \; r}{t}\mspace{14mu} {and}} = \frac{2\mu \; Q}{\pi \; r^{3}}}},$

where P is blood pressure, a is the intramural stress, r is the lumenradius, t is the wall thickness, Q is the lumen flow rate, τ is the wallshear stress, and μ is the dynamic viscosity of blood. Additionally,progression/regression modeling may the addition of data from serialimaging studies of the same patient/AVM at one or more subsequent times,and using that information to inform/update the model.

Alternatively, one embodiment of step 212 may include using a numericalmethod including finite element, finite differences, and/or spectralexpansion methods to predict remodeling. This may involve first creatinga geometric model of the lumen blood vessel wall, then meshing theresultant model and solving the partial differential equations governingstress equilibrium and mass conservation to achieve a homeostatic state.Another alternative may be to use machine learning methods where therelationship between the thickness and radius of the blood vessels dueto changes in flow on a number of different AVMs may be first learnedfrom data. This may be performed by constructing a map between geometricvariables, blood flow, blood pressure, and the remodeled blood vesselradius and wall thickness. Subsequently, this map may be used to predictthe remodeled geometry based on all the input features, includingfeatures extracted from geometry (e.g., original lumen radius, size andnumber of draining blood vessels, size and number of feeding bloodvessels, resistance of downstream vasculature, blood supply, andparameters derived from hemodynamics including blood pressure, and wallshear stress). An increase in the overall AVM radius and/or the radiusof its individual vessels may indicate progression and a reduction mayindicate regression.

In one embodiment, step 214 may include outputting a plurality offactors to a display, including but not limited to, progression and/orregression of vessel size and states, intramural stresses and wall shearstresses of the vessels, vessel location and proximity to vital organs,pressure of the blood in the veins, and capillary perfusion. Onerepresentation may be an electronic chart summarizing the benefitsand/or risks of one or more surgical interventions or other treatments,and the risk of leaving the AVM untreated. The following variables maybe examples of variables that may be included in a displayed output: (a)differences in rupture risk before and after treatment, (b) location ofcritical organs and/or blood vessels in the vicinity of the AVM, (c)difficulty in site access which may be quantified using tortuosity ofthe vessel and the size of the AVM, (d) a patient-specific map of wallshear stress and intramural stress before and after intervention, and(e) mass effect characteristics quantifying wall displacement (e.g.,encroachment into surrounding tissue) and forces exerted on the tissue.

The predicted remodeled geometry based on the analysis in step 212 maybe output to a display in step 214. The output may be the new remodeledpatient-specific geometry or the changes in vessel geometry at salientlocations including the vessels feeding or draining the AVM. Inaddition, the venous pressure and tissue blood perfusion may be outputto a display. Any other output that may be available from the simulationmay be included.

FIG. 4 depicts an exemplary, more specific embodiment of a method 400for providing risk assessment and treatment option assessment ofcerebral AVMs either using MRI or selective angiography techniques.Cerebral AVMs may involve abnormal connections between the arteries andveins of the brain. Those connections shunt the capillary vessels andmay pose risks such as cerebral hemorrhaging and epilepsy. The methoddepicted in FIG. 4 may include any of the steps and features describedabove in connection with FIG. 3 and within a system described in FIG. 2.

Step 402 of method 400 may include acquiring non-invasive images of thearterial and venous system proximate the location of an AVM. In anembodiment, this may include obtaining one or more digitalrepresentations/images encompassing some or all of the arterial andvenous vasculature system of the brain, including such vasculatureencompassing the location of the AVM. More specifically, step 402 mayinclude collecting input data acquired using a non-invasive techniqueincluding MRI or CT scans, ultrasound (extra and intravascular), 3Dultrasound, duplex ultrasound and/or optical coherence tomography (OCT).The locations of AVMs may be characterized by the number and size of theblood vessels that connect arteries to veins.

Optionally, step 404 may include performing a selective angiography. Anon-invasive method, such as MRI or CT scan, may show only a white spotor blurred area near the location of an AVM, due to multiple overlappingvessels of various sizes comprising the AVM obscuring visualization ofindividual vessels within the AVM or due to limitations of theresolution of the imaging modality. In such cases, step 404 may includeperforming a selective angiography where one or more blood vessels maybe clipped and a contrast agent may be selectively administered to aparent artery to aid in identification of the vessels. This may enableisolation of the blood vessels and aid in an accurate patient-specificreconstruction of the blood vessels near the AVM site.

Optionally, step 406 may include performing a selective venography toisolate blood vessels and improve visualization. This step may beperformed with or without the angiography of step 404. The draining ofveins of the AVM, which may have high blood flow, may be bettervisualized and reconstructed using contrast enhanced 3D MR venography.Other visualization techniques may be used to isolate vessels andimprove visualization of vessels proximate an AVM. In addition, thisstep may include a variety of techniques for directly inputting vesselsor segments of vessels which cannot be visualized.

Step 407 includes generating a patient specific model of the vasculatureproximate the AVM, based on the images and other data obtained in steps402, 404, and 406, using any suitable technique, such as ones mentionedabove and in incorporated by reference U.S. Pat. Nos. 8,315,812 and9,042,613 and U.S. Patent Application Publication No. 2014/0073977.

Step 408 may include identifying the blood vessels within the model thatmay be responsible for feeding and draining blood to and from the AVM.These vessels may be identified by examining all of the blood vesselsconnecting the AVM identified in steps 402, 404, and 406 to bloodvessels which may not be a part of the AVM. Step 408 may further includevisualizing the feeding and draining blood vessels in a display. The AVMitself may be localized with an image segmentation algorithm, an imagethreshold algorithm, or any other suitable method.

Step 410 may include quantifying patient risk without treatment of thecerebral AVM, by performing blood flow simulations. Patient risk may bequantified using either anatomical information or functionalinformation, as discussed above. For example, the Spetzler-Martin grademay be used in estimating the risk to a patient caused by an AVM. TheSpetzler-Martin grading system allocates points for various features ofintracranial AVMs, resulting in a score between 1 and 5. The scorecorrelates with operative/treatment outcome. The factors that may beconsidered in quantifying patient risk in the Spetzler-Martin gradingsystem include, but are not limited to: (i) the size of the AVM (1, 2,or 3 points depending on the size), (ii) the presence of an adjacencyeloquent cortex (e.g., the cortex may be involved in sensoryprocessing—1 point), and (iii) the presence of draining veins(superficial—0 points, and/or deep vein—1 point). The Spetzler-Martingrade may be first calculated and stored in a memory.

Step 410 may further include performing blood flow simulations which mayaid in predicting functional estimates of patient risk. The blood flowsimulations may be performed through the cerebral AVM using thepatient-specific model reconstructed in step 407, and subsequentlycreating a finite element model and solving the Navier-Stokes equationsfor mass and momentum balance of blood. Inlet flow boundary conditionsmay be assigned if available, and a pressure boundary condition may beassigned at the arterial inlets to the model. Pressure or resistanceboundary conditions may be applied at the venous outlets that model theresistance downstream of the veins to their draining site.

Alternatively, step 410 may include solving one dimensional blood flowequations, and/or reduced order models including solving theHagen-Pouiseuille equations with stenosis models. Bernoulli's equationsalso may be used. Alternatively, a machine learning approach may beused. The net stress acting on the vascular wall (including bothintramural and wall shear stress) may be calculated from the bloodvelocities and from material properties of the vascular wall.Furthermore, the mass effect may be calculated by assigning materialproperties to the tissue surrounding the cerebral system, and computingthe forces exerted by the motion of cerebral AVMs on the external tissueusing Fluid-Structure interaction simulations.

In one embodiment, step 412 may include performing post-treatmentsimulations. To quantify the risk of intervention, blood flowsimulations may be performed in a virtually treated vascular model.Treatment options may be based on embolization of multiple combinationsof draining and feeding vessels to the AVM to ensure minimization ofrisk factors. Simulations may be performed in the post-treatmentvascular model, and the quantities identified above in step 410 may becomputed. Alternatively, step 412 may include using machine learningmethods or reduced order models. The risk of rupture may be calculatedin the post-treatment geometry.

Step 412 may be repeated for any number of candidate treatmentconfigurations as identified by the physician. An optimization method(e.g., pattern search methods or Nelder-Mead algorithm) may be used toautomatically calculate the best configuration that minimizes the riskof rupture or any other undesirable outcome.

In one embodiment, step 414 may include predicting progression andregression of the AVM. Step 414 may further include constructing afinite element mesh of the vessel walls and performing a fluid structureinteraction simulation to compute wall shear stress and intramuralstress. Subsequently, progression and regression of the AVM may bepredicted by solving the growth and remodeling equations on the vesselwall and calculating the radius and thickness of the AVM or vesselscomprising the AVM at homeostasis. Step 414 also may involve modelingthe cerebral vascular wall properties, imposing the homeostatic stressstate in the vascular walls, and solving the stress equilibriumequations until the stresses reach the homeostatic state. The resultantgeometry (blood vessel radius and thickness) may represent the remodeledhomeostatic state. An increase in the radius of the vessels may imply aprogression of the AVM, and a reduction thereof may indicate aregression in the AVM.

Step 416 may include generating a list of benefits and/or risks of oneor more treatment methods and/or of not treating the AVM. A list offactors that may influence the treatment plan may be output to thephysician. The list may include, but is not limited to: (a) anatomicalrisk factors including the Spetzler-Martin grade, which as describedabove may be based on AVM size or size of the vessels comprising the AVM(e.g., small, medium or large), location in the brain, and whethersuperficial or deep veins are involved; (b) functional risk factor usinghemodynamic simulations and quantifying difference in rupture risk withand without treatment; (c) difference in venous pressure post- andpre-treatment; (d) difference in tissue perfusion before and aftertreatment; and (e) predicting the remodeled blood vessel radius andthickness, and highlighting the differences in/around the AVM. Step 418may include outputting the predicted progression and/or regressionand/or risks and benefits to an electronic storage medium or to adisplay. This method also may include follow-up imaging to evaluate theresults of treatment and predict risk of recurrences.

It will be apparent to those skilled in the art that variousmodifications and variations can be made in the disclosed retrievaldevices and methods without departing from the scope of the disclosure.Other aspects of the disclosure will be apparent to those skilled in theart from consideration of the specification and practice of the featuresdisclosed herein. It is intended that the specification and examples beconsidered as exemplary only.

1-20. (canceled)
 21. A computer implemented method for assessing anarterio-venous malformation (AVM), the method comprising: receiving apatient-specific three-dimensional anatomic model of a portion of ananatomy of a patient, including one or more blood vessels having one ormore vessel walls; identifying, using a computer processor, features ofthe one or more blood vessels of the patient-specific model associatedwith the AVM; calculating one or more vessel wall properties of the oneor more vessel walls, using the determined blood flow characteristic byinputting the identified features into a trained machine learningalgorithm; estimating a risk of an undesirable outcome caused by the AVMbased on the calculated vessel wall properties; modifying thepatient-specific three-dimensional anatomic model to evaluate one ormore treatments of the AVM; identifying, using a computer processor,features of the one or more blood vessels of the modifiedpatient-specific three-dimensional anatomic model associated with atreatment of the AVM; and calculating one or more vessel wall propertiesat one or more points through the modified patient-specificthree-dimensional anatomic model, by inputting the identified featuresinto a trained machine learning algorithm.
 22. The method of claim 21,further comprising: determining a blood flow characteristic at one ormore points of the patient-specific model associated with the AVM;creating feature vectors comprising the identified features and thedetermined blood flow characteristics; and calculating one or morevessel wall properties of the one or more vessel walls, by inputting theidentified features into a trained machine learning algorithm.
 23. Themethod of claim 22, wherein the blood flow characteristic is determinedby performing a blood flow simulation through the one or more identifiedblood vessels having the one or more vessel walls of thepatient-specific three-dimensional anatomic model.
 24. The method ofclaim 23, wherein the blood flow simulation includes simulating orpredicting progression, regression, or remodeling of the AVM.
 25. Themethod of claim 21, further comprising: determining a second blood flowcharacteristic at one or more points of the modified patient-specificmodel associated with one or more treatments of the AVM; creatingfeature vectors comprising the identified features and the determinedsecond blood flow characteristic; and calculating one or more vesselwall properties of the one or more vessel walls, by inputting thecreated feature vector into a trained machine learning algorithm. 26.The method of claim 21, wherein the machine learning algorithm istrained by: receiving data from a plurality of individuals comprising:(1) features at one or more points of a three-dimensional modelassociated with an AVM of one or more blood vessels having one or morevessel walls, and (2) one or more vessel wall properties at pointscorresponding to the one or more points of the three-dimensional model;associating the features at the one or more points of athree-dimensional model associated with an AVM with the one or morevessel wall properties at points corresponding the one or more points;and training a machine learning algorithm that can predict one or morevessel wall properties at points of a three-dimensional model of one ormore blood vessels having one or more vessel walls from an input offeatures at one or more points of a three-dimensional model, based onlearning from the associated features.
 27. The method of claim 21,wherein the patient-specific model is based on images of at least aportion of a vascular system of the patient.
 28. The method of claim 21,wherein the undesirable outcome includes one or more of rupture of ablood vessel, a mass effect, an increase in venous pressure, and achange in tissue perfusion.
 29. The method of claim 21, whereinevaluating the one or more treatments includes: identifying one or moreblood vessels for treatment; determining an effect on blood flow causedby treatment of the one or more blood vessels; and assessing a changethe risk of the undesirable outcome.
 30. The method of claim 29, whereinthe treatment includes one or more of embolization of the one or moreblood vessels, ablation of the one or more blood vessels, surgicalremoval of all or part of the AVM, or radiosurgery.
 31. The method ofclaim 21, further comprising estimating the risk of the undesirableoutcome based on a homeostatic vessel state.
 32. A system for assessingan arterio-venous malformation (AVM), the system comprising: a datastorage device storing instructions for assessing an arterio-venousmalformation (AVM); and a processor configured to execute theinstructions to perform a method comprising the steps: receiving apatient-specific three-dimensional anatomic model of a portion of ananatomy of a patient, including one or more blood vessels having one ormore vessel walls; identifying, using a computer processor, features ofthe one or more blood vessels of the patient-specific model associatedwith the AVM; calculating one or more vessel wall properties of the oneor more vessel walls, using the determined blood flow characteristic byinputting the identified features into a trained machine learningalgorithm; estimating a risk of an undesirable outcome caused by the AVMbased on the calculated vessel wall properties; modifying thepatient-specific three-dimensional anatomic model to evaluate one ormore treatments of the AVM; identifying, using a computer processor,features of the one or more blood vessels of the modifiedpatient-specific three-dimensional anatomic model associated with atreatment of the AVM; and calculating one or more vessel wall propertiesat one or more points through the modified patient-specificthree-dimensional anatomic model, by inputting the identified featuresinto a trained machine learning algorithm.
 33. The system of claim 32,wherein the machine learning algorithm is trained by receiving data froma plurality of individuals comprising: (1) features at one or morepoints of a three-dimensional model associated with an AVM of one ormore blood vessels having one or more vessel walls, and (2) one or morevessel wall properties at points corresponding to the one or more pointsof the three-dimensional model; associating the features at the one ormore points of a three-dimensional model associated with an AVM with theone or more vessel wall properties at points corresponding the one ormore points; and training a machine learning algorithm that can predictone or more vessel wall properties at points of a three-dimensionalmodel of one or more blood vessels having one or more vessel walls froman input of features at one or more points of a three-dimensional model,based on learning from the associated features.
 34. The system of claim32, wherein the patient-specific model is based on images of at least aportion of a vascular system of the patient.
 35. The system of claim 32,wherein the undesirable outcome includes one or more of rupture of ablood vessel, a mass effect, an increase in venous pressure, and achange in tissue perfusion.
 36. The system of claim 32, wherein theblood flow simulation includes simulating or predicting progression,regression, or remodeling of the AVM.
 37. The system of claim 32,wherein evaluating the one or more treatments includes: identifying oneor more blood vessels for treatment; and determining an effect on bloodflow caused by treatment of the one or more blood vessels.
 38. Thesystem of claim 37, wherein the treatment includes one or more ofembolization of the one or more blood vessels, ablation of the one ormore blood vessels, surgical removal of all or part of the AVM, orradiosurgery.
 39. A non-transitory computer-readable medium storinginstructions that, when executed by a computer, cause the computer toperform a method for assessing an arterio-venous malformation (AVM), themethod comprising: receiving a patient-specific three-dimensionalanatomic model of a portion of an anatomy of a patient, including one ormore blood vessels having one or more vessel walls; identifying, using acomputer processor, features of the one or more blood vessels of thepatient-specific model associated with the AVM; calculating one or morevessel wall properties of the one or more vessel walls, using thedetermined blood flow characteristic by inputting the identifiedfeatures into a trained machine learning algorithm; estimating a risk ofan undesirable outcome caused by the AVM based on the calculated vesselwall properties; modifying the patient-specific three-dimensionalanatomic model to evaluate one or more treatments of the AVM;identifying, using a computer processor, features of the one or moreblood vessels of the modified patient-specific three-dimensionalanatomic model associated with a treatment of the AVM; and calculatingone or more vessel wall properties at one or more points through themodified patient-specific three-dimensional anatomic model, by inputtingthe identified features into a trained machine learning algorithm. 40.The computer-readable medium of claim 39, wherein the machine learningalgorithm is trained by receiving data from a plurality of individualscomprising: (1) features at one or more points of a three-dimensionalmodel associated with an AVM of one or more blood vessels having one ormore vessel walls, and (2) one or more vessel wall properties at pointscorresponding to the one or more points of the three-dimensional model;associating the features at the one or more points of athree-dimensional model associated with an AVM with the one or morevessel wall properties at points corresponding the one or more points;and training a machine learning algorithm that can predict one or morevessel wall properties at points of a three-dimensional model of one ormore blood vessels having one or more vessel walls from an input offeatures at one or more points of a three-dimensional model, based onlearning from the associated features.